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Genetic algorithm based adaptive offloading for improving IoT device communication efficiency

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Abstract

Improving the communication of Internet of Things (IoT) network is a challenging task as it connects a wide-range of heterogeneous mobile devices. With an extended support from cloud network, the mobile IoT devices demand flexibility and scalability in communication. Increase in density of communicating devices and user request, traffic handling and delay-less service are unenviable. This manuscript introduces genetic algorithm based adaptive offloading (GA-OA) for effective traffic handling in IoT-infrastructure-cloud environment. The process of offloading is designed to mitigate unnecessary delays in request process and to improve the success rate of the IoT requests. The fitness process of GA is distributed among the gateways and infrastructure to handle requests satisfying different communication metrics. The process of GA balances between the optimal and sub-optimal solutions generated to improve the rate of request response. Experimental results prove the consistency of the proposed GA-OA by improving request success ratio, achieving lesser complexity, delay and processing time.

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Authors and Affiliations

  1. Human-Centered Computing Research Lab, School of Computing, Universiti Utara Malaysia, 06010, Sintok, Malaysia

    Azham Hussain

  2. Melange Academic Research Associates, Puducherry, India

    S. V. Manikanthan & T. Padmapriya

  3. Department of EEE, SAINTGITS College of Engineering, Pathamuttom, Kottayam, Kerala, India

    Mahendran Nagalingam

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  1. Azham Hussain
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  2. S. V. Manikanthan
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  3. T. Padmapriya
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  4. Mahendran Nagalingam
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Correspondence to Azham Hussain.

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Hussain, A., Manikanthan, S.V., Padmapriya, T. et al. Genetic algorithm based adaptive offloading for improving IoT device communication efficiency. Wireless Netw 26, 2329–2338 (2020). https://doi.org/10.1007/s11276-019-02121-4

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